import torch
from torch import nn


class WNormLoss(nn.Module):

    def __init__(self, start_from_latent_avg=True):
        super(WNormLoss, self).__init__()
        self.start_from_latent_avg = start_from_latent_avg

    def forward(self, latent, latent_avg=None):
        if self.start_from_latent_avg:
            latent = latent - latent_avg
        return torch.sum(latent.norm(2, dim=(1, 2))) / latent.shape[0]
